Using Big Data Analytics and Heatmap Matrix Visualization to Enhance Cryptocurrency Trading Decisions

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study m...

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Bibliographic Details
Published in:Applied sciences 2024-01, Vol.14 (1), p.154
Main Authors: Ni, Yensen, Chiang, Pinhui, Day, Min-Yuh, Chen, Yuhsin
Format: Article
Language:English
Subjects:
Analysis
Behavior
Big Data
big data analytics
Bollinger Bands
contrarian strategies
Crypto-currencies
cryptocurrency spot prices
Data analysis
Digital currencies
Efficiency
Efficient markets
Financial instruments
Forecasts and trends
heatmap visualization
Hypotheses
Investments
Prices
Profitability
Profits
Research design
round-turn trading
Securities markets
Securities trading
Spot market
Stock exchanges
Visualization (Computers)
Volatility
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Summary:Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14010154